bienei_V6.4

本算法采用matalab编写，实现了基于复Hadamard变换的盲数字水印算法(The algorithm adopts matalab writing, Hadamard transform based on complex blind watermarking algorithm is proposed)

CR中基于能量检测的频谱感知matlab程序仿真(CR in the detection of spectrum sensing based on energy simulation program)

无线传感器网络定位算法，包括现有的大部分算法！(Location algorithm for wireless sensor networks, including most of the existing algorithms!)

说明：  The Internet of Things (IoT) and machine-to-machine (M2M) communication will play an important role in future communications, but there is currently no ultra-reliable low-latency wireless communication theory that guides its design. For highly mobile vehicle-to-vehicle (V2V) wireless communication scenarios, asynchronous transmission should also be accepted. Therefore, it is particularly urgent to study ultra-reliable low-latency wireless transmission technology that satisfies asynchronous transmission. Universal Filter Multi-Carrier (UFMC) is a new type of filtering wireless transmission mechanism that meets this character

Data mining, machine learning

MATLAB 实现ID3算法 (ID3 algorithm in MATLAB)

this code reduce the dimentional of feature space using combine sparse matrix+PCA for classification eeg signal. more detail exixst inside the code. this code tested and work properly

该程序用matlab写成，已包含vof（根据Hirt的方法）内容，但该部分尚不成熟。对于不包含自由面的计算比较成功。(The program written with matlab, included vof (according to Hirt' s method) content, but that part is not yet mature. For the calculation of the free surface does not contain more successful.)

This is the alternating direction method of multipliers by Zouchen Lin

逐步判别分析的主函数 用于分类 [sel,c,c0,re,P]=StepclaassV2(data,[50 50 50],data,10) 输入变量 x为训练集.每行为一个样本，每列为一个变量. Class_x,为训练集的分类情况，一行，为各类样本数量,例如[5 6 9] Test为待分类样本. 输出变量：sel为选择的变量序号，c,c0为拟合出的判别函数.re为对Test的判别结果,P为其后验概率. author 王新 2012.4.8 例子Sample: load fisheriris x=meas Class_x=[50,50,50] Test=meas(1:5,:) Fa1=4 [sel,c,c0,re,P]=StepclaassV2(x,Class_x,Test,Fa1)( Stepwise discriminant analysis for the classification of the main function [sel, c, c0, re, P] = StepclaassV2 (data, [50 50 50], data, 10) input variables x as the training set, each conduct a sample of each as a variable. Class_x, the classification for the training set, a row, the number of samples for all types, for example, [5 6 9] Test sample to be classified. output variable: sel for the selected variables serial number, c, c0 is fitted discriminant function. re discrimination results for the Test, P its posterior probability. author Wang 2012.4.8 examples Sample: load fisheriris x = meas Class_x = [50,50,50] Test = meas (1:5, :) Fa1 = 4 [sel, c, c0, re, P] = StepclaassV2 (x, Class_x, Test, Fa1))